Intelligent learning systems are systems that attempt to assist students in achieving specific learning goals. To date, these systems have mainly used a computerized teaching approach that mirrors the approach taken in brick-and-mortar classrooms. Each student is presented with the same lecture, content, and assessment, regardless of learning style, intelligence, or cognitive characteristics.
Advances in intelligent learning systems have been limited, and are usually applied to logic-based topics such as mathematics, where the content that is served to each student is based on a pre-determined course-specific decision tree that is hard-coded into the system. If a first student and a second student each fail the same assessment by missing the same questions, both students will be presented with the same remedial materials as dictated by the decision tree.
Online courses are examples of “containers” that may employ adaptive learning technology to achieve a specific goal. The container for each course is designed to include all of the information required to achieve success within the course. For example, the content required for achieving the goals of a particular Mathematics course would be directly associated with the container of the Mathematics course. The container for each course also includes the logic that determines which content (in that finite set of content that is directly associated with the container) should be delivered to the student. By performing in a particular way, the student merely traverses down a pre-existing path through the course's logical hierarchy that is hard-coded into the course's container.
Current intelligent learning systems focus on high-level goals, and each student is directed down the same logical path in order to determine which content is provided to the student. For example, an adaptive learning tool may be designed to teach a student a course on the fundamentals of calculus. The designer of the tool will assume that the student possesses the foundational knowledge of mathematics required to begin the course, but since the tool is unaware of any other attribute of the student, the tool may provide a certain amount of “review” information as a means of calibration. All students receive the same information, and performance on assessments will dictate which learning item is presented to the student next.
Current intelligent learning systems “hard-code” these learning items to the logic used to determine the next step for the user. For example, a student that completes “Task A” will always be presented with the video item named “Video_23.mov” to begin the next learning section. Students that do not achieve a predetermined level of success with an assessment or a particular task will always be presented with the multimedia item “Remediation_13.swf” as a means of re-teaching the subject matter required for that particular task.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.